What is the National Data Service? National consortium to bring together infrastructure supporting the publication, discovery, and reuse of data Coordinating as a broad community of data stakeholders Large-scale Data Service Interoperability Distributed cloud and compute Innovation in the gaps: services, software, integration Incubator of Data Projects & Pilots Quick start sandbox Coordination resources

Who We Are: NDS Consortium Communities Astronomy, Biology, Engineering, Geoscience, Information Science, Material Science, Medicine, Social Science Universities, Libraries, Archives, and Publishers CU Boulder, Harvard, Indiana, Johns Hopkins, Notre Dame, Purdue, UC San Diego, UIC, UIUC, U Michigan, ICPSR … Nature, Science, APS, IEEE, PLOS, Elsevier, … Computing and Data Centers/Cyberinfrastructure ANL, NCSA, PSC, SDSC, TACC Brown Dog, Data Excacell, DataONE, DFC, CyVerse, GABBs, IN-CORE, iRODS, Globus, SciServer, SEAD, Terra Populus, TERRA REF, Whole Tale, LSST, LIGO, ...

From the Internet to the DataNet An analogy from the Research Data Alliance compares the current situation faced by scientists with data management tools to that of early Internet users, before standard protocols such as TCP/IP and HTTP or markup languages such as HTML were widely adopted. While Internet technologies vary widely, with many choices available for each component, this tends to not affect the user in terms of overall capabilities. A user can choose one of any number of browsers and still interoperate with other web technologies as they communicate via standard protocols and interfaces such as HTTP and HTML. This works so well, we take it for granted. Data management software is similar with archive software filling a role analogous to that of a web server and portals filling the role of browsers with a key distinction being the lack of standard protocols and interfaces. The NDSC aims to foster the establishment of a broadly usable and sustainable data management pipeline and a National Data Service by directly addressing interoperability between developed data management components and pursuing the implementation of standardized interfaces between them.

Shifting to an infrastructure perspective What are the greatest challenges we are facing in implementing solutions for research data management? Shifting to an infrastructure perspective from project/prototype emphasis to sustained operations, integrated services, evolution of software, coordination of providers Urban legends Costs Researchers won’t publish More formal metadata is required Reuse won’t happen Good enough is already here…

How has your work in data management enabled scientific progress? Each consortium member has their success stories, e.g. National Data Service NDS Labs Hosting data apps and services for demo, testing, and classes Dozens of publications, 4+ TB of data, research published in top journals Data rescue, ongoing collection development, thesis research Hydrology, biology, modeling, cyberinfrastructure, digital humanities,… ‘Viral’ use Computation Over Data TERRA-REF DataDNS astronomy demonstrations of general mechanism

Coordination across stakeholders is critical What key best practices has your organization discovered or enabled as you implement data management solutions? Coordination across stakeholders is critical Researchers, developers, libraries, resource providers, … More value as an ecosystem Universally Accessible Data Publications Labs, DataDNS Socio-technical solutions PIDs critical to citation and credit Services/hosting important in supporting ownership Integrating viz/analysis/metadata extraction, instruments, curation, and publication adds value and drives adoption

More info: http://nationdataservice.net Christine Kirpatrick (christine@sdsc.edu), NDS Executive Director Jim Myers (myersjd@umich.edu), Technical Advisory Board Member Join the NDS mail list, slack channel, … Thanks to Christine and Kenton McHenry for discussion/slides for this presentation